A combined qSOFA-lactate model for sepsis-related mortality prediction: a small step towards an elusive answer?
Authors: Rahuldeb Sarkar
Journal: The Lancet Global Health (2022)

Sepsis, or dysregulated host immune response to infection, is a major killer worldwide, responsible for nearly 20% of all deaths globally.1Rudd KE Johnson SC Agesa KM et al.Global, regional, and national sepsis incidence and mortality, 1990–2017: analysis for the Global Burden of Disease Study.Lancet. 2020; 395: 200-211Summary Full Text Full Text PDF PubMed Scopus (1423) Google Scholar Therefore, early identification of patients at increased risk of developing sepsis is important for initiation of appropriate care pathways, and to reduce preventable deaths. However, this identification process requires additional resource allocation, which even high-income countries (HICs) struggle with and contemplate upon.2Slawomirski L Auraaen A Klazinga N The economics of patient safety. OECD, Paris2017Google Scholar Efficient prediction tools like the Sequential Organ Failure Assessment (SOFA) score, involving multiple laboratory parameters, have consistently shown high precision, but are challenging to implement in low-income and middle-income countries (LMICs) due to logistical and operational issues. The Sepsis-3 task force showed the quick SOFA (qSOFA) score to have good predictive validity in identifying patients with suspected infection at high risk of mortality in patients outside intensive care units in a HIC setting. This has been subsequently corroborated in LMIC settings.3Seymour CW Liu VX Iwashyna TJ et al.Assessment of clinical criteria for sepsis: for the third international consensus definitions for sepsis and septic shock (Sepsis-3).JAMA. 2016; 315: 762-774Crossref PubMed Scopus (1997) Google Scholar, 4Rudd KE Seymour CW Aluisio AR et al.Association of the quick Sequential (sepsis-related) Organ Failure Assessment (qSOFA) score with excess hospital mortality in adults with suspected infection in low- and middle-income countries.JAMA. 2018; 319: 2202-2211Crossref PubMed Scopus (111) Google Scholar However, the most recent Surviving Sepsis Guideline strongly recommends against using qSOFA as a single screening tool for sepsis because of low sensitivity.5Evans L Rhodes A Alhazzani W et al.Surviving sepsis campaign: international guidelines for management of sepsis and septic shock 2021.Intensive Care Med. 2021; 47: 1181-1247Crossref PubMed Scopus (350) Google Scholar Therefore, the search for a simplistic effective tool for resource-limited settings continues. In The Lancet Global Health, Shelton Wright and colleagues6Wright SW Hantrakun V Rudd KE et al.Enhanced bedside mortality prediction combining point-of-care lactate and the quick Sequential Organ Failure Assessment (qSOFA) score in patients hospitalised with suspected infection in southeast Asia: a cohort study.Lancet Glob Health. 2022; 10: e1281-e1288Summary Full Text Full Text PDF PubMed Scopus (1) Google Scholar assessed a relatively simple bedside prediction model for infection-related mortality in a LMIC setting. The tool was derived from the large Ubon-sepsis cohort, combining qSOFA and point-of-care venous lactate in southeast Asia. In patients with suspected infection, the authors assigned a score of 0 (for lactate concentration <2·0 mmol/L), 1 (for lactate concentration ≥2·0 to <4·0 mmol/L), or 2 (for lactate concentration ≥4 mmol/L) and added this to the usual qSOFA score (0–3), resulting in a 5-point scoring system. The model was externally validated on the multinational SEAICRN cohort. In addition to having good calibration, the qSOFA-lactate score provided better 28-day mortality discrimination than the qSOFA alone and similar discrimination to modified SOFA. There was marginal improvement in 28-day mortality discrimination with qSOFA-lactate score over ternary lactate score in the derivation cohort, but these models showed similar discrimination in the validation dataset. Furthermore, a combined model of qSOFA-lactate and baseline risk factors had a better mortality discrimination than the combined model of qSOFA and baseline risk factors. This discrimination was not better than that of a model combining baseline risk factors with ternary lactate. The authors should be commended for developing the model in an LMIC setting, where clinicians often have to extrapolate from tools built in HICs, with suboptimal results.7Stephan BCM Pakpahan E Siervo M et al.Prediction of dementia risk in low-income and middle-income countries (the 10/66 Study): an independent external validation of existing models.Lancet Glob Health. 2020; 8: e524-e535Summary Full Text Full Text PDF PubMed Scopus (18) Google Scholar Data completeness was close to 100% for most relevant parameters, including complete availability of the 28-day mortality outcome data, even after hospital discharge. A tool such as this might be useful in a peripheral health-care facility in LMICs, to identify patients at risk of deterioration, for transferring out to a referral hospital, and also for triaging in emergency rooms in better-equipped hospitals. Use of venous point-of-care lactate, which is easier than arterial lactate to measure logistically, also makes this model more appealing. Different score cutoffs can be used depending on the trade-offs that a particular health system is willing to accept. While interpreting the results, we must be mindful of some limitations of this study. First, a large proportion of patients, as high as 77%, in both the derivation and validation cohorts were transferred into the tertiary study hospitals where participants were enrolled. It is likely that these patients might not be entirely representative of patients who were directly admitted to a health-care facility on day 1. It is, however, reassuring that the model performed well in patients without history of transfer. Second, the same numerical qSOFA-lactate score was associated with different mortality risks depending on the score components. For example, a qSOFA-lactate score of 2, entirely contributed by qSOFA score (qSOFA score of 2, ternary lactate score of 0), was associated with a 28-day mortality risk of 8%. However, with a qSOFA-lactate score of 2 entirely contributed by lactate scale (qSOFA score of 0, ternary lactate score of 2) was associated with a mortality of 27%. This difference warrants relevant subgroup analysis in implementation studies. Third, it would be interesting to see how purely clinical parameter-based models (eg, National Early Warning Score) perform compared with the qSOFA-lactate model, as they have been previously shown to perform well in other LMIC settings.8Bonnewell JP Rubach MP Madut DB et al.Performance assessment of the universal vital assessment score vs other illness severity scores for predicting risk of in-hospital death among adult febrile inpatients in northern Tanzania, 2016–2019.JAMA Netw Open. 2021; 4e2136398Crossref PubMed Scopus (2) Google Scholar Fourth, the lactate-only model had similar mortality discrimination compared with qSOFA-lactate in the validation cohort. An analysis of feature importance in subsequent studies could shed more light on this. A lactate-only tool could be attractive, as it is devoid of intraobserver and interobserver variability associated with measurement of clinical parameters. Finally, as the authors point out, absence of surgical patients in the dataset limits generalisability to this important patient group. For a prediction tool, successful implementation process is often a bridge to cross in achieving improved patient outcomes, due to system-based factors not considered in the model development. The pathway from building an algorithm to improved outcomes has previously been described.9Pencina MJ Goldstein BA D'Agostino RB Prediction models –– development, evaluation, and clinical application.N Engl J Med. 2020; 382: 1583-1586Crossref PubMed Scopus (46) Google Scholar, 10Harris AHS Path from predictive analytics to improved patient outcomes: a framework to guide use, implementation, and evaluation of accurate surgical predictive models.Ann Surg. 2017; 265: 461-463Crossref PubMed Scopus (21) Google Scholar The same processes should apply to the qSOFA-lactate model before it is incorporated in widespread clinical decision support. Another important task would be to assess this model in large cohorts within HICs, where widespread adoption of electronic health records enables examination of simple prediction tools in a relatively short time. Replication of similar results will be necessary for reassurance that the clinical underpinning of the model is robust across all settings. I declare no competing interests. Enhanced bedside mortality prediction combining point-of-care lactate and the quick Sequential Organ Failure Assessment (qSOFA) score in patients hospitalised with suspected infection in southeast Asia: a cohort studyIn southeast Asia, rapid, bedside assessments based on point-of-care lactate concentration combined with the qSOFA score can identify patients at risk of sepsis-related mortality with greater accuracy than the qSOFA score alone, and with similar accuracy to a modified SOFA score. Full-Text PDF Open Access